In [1]}, the neighborhood function is interpreted as the
variational posterior in a Expectation-Minization (EM, [2]})
procedure. Therefore, they propose to minimize the following (opposite) ELBO to
train SOMs
\(F\left(\rho , \theta \right) &= \int \int \ln \left(\frac{q_{z|x}^{\rho }}{p_{x, z}^{\theta }}\right)q_{z|x}^{\rho }\mathrm {d} z p_{x} \mathrm {d} x \\&= \underbrace{\int \mathcal {D}\left(q_{z|x}^{\rho }, p_{z|x}^{\theta }\right)p_{x}\mathrm {d}x}_{\text{organization}} + \underbrace{\mathcal {D}\left(p_{x}, p_{x}^{\theta }\right)}_{\text{modelization}},\) 
